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In the following pages I would like to reflect on conceptions of community and collective endeavor at stake in contemporary struggles over the promise and perils of big data collection and analytics. Proponents of big data analysis—including scholars, academic administrators, government officials, health care workers, and private sector executives—frequently frame it not just as an intensification of traditional scholarly methods or as a corrective to less accurate tools but also as a social good, as a means for overcoming the kinds of interpretive biases, frictions, and ambiguities often viewed as obstacles to good governance or effective social organization. My interest in this topic stems first from a concern for the relationship or intersection between the enthusiasm for big data that we see today in various corners of academe, and the rise of big data in neoliberal economic discourses about knowledge production and communal life. Yet if appeals to big data in the university—and in the humanities in particular—display anxieties about relevance and survival in a neoliberal environment that privileges empirical, “actionable” evidence, they are not reducible to these concerns alone. While some big data enthusiasts espouse forms of instrumental rationality long denounced by critics shaped in the interpretive traditions of the Frankfurt school, postcolonial studies, and feminist and queer studies, others view big data as an extension of, and tool for, such critique, as a necessary turn in our contemporary moment—a globalized moment whose structures, modes of interaction, and major problems, such as climate change, urgently require massive temporal and spatial scales of analysis.

In considering the various investments at stake in debates over big data, and debates over big data in the humanities and literary studies more specifically, I would like to keep in mind Édouard Glissant’s concept of “donner-avec,” or “giving-on-and-with,” a mode of knowledge that Glissant posited as a generous hermeneutics, an ethical alternative to the grasping, appropriative logic of “comprehension” underpinning instrumental rationality broadly, and colonial or neocolonial political projects more specifically (1997, 191-92). What understandings of give and take structure or motivate investments in big data? In what ways, and to whom, does it matter whether we conceive of data analysis as seizing or grasping the given, as respecting or reciprocating what is given, or as producing or co-producing a gift? [End Page 459]

Motivated by concerns to describe and foster creative processes of cultural encounter, renewal and relation, Glissant’s works frequently linger on the question of how we relate to “givens,” to data or evidence we apprehend through the senses or through calculation and which we use as presumptions, as grounds for building conclusions. The etymological connection between data and giving echoes more loudly in French, which renders the idea of data and givens by a single term, données. Data, this etymological reflection reminds us, never merely exist, but must be given, and taken, constructed through acts of identifying, gathering, compiling, and computing. The English expression “to take something as a given” recalls that even that which is seemingly merely available or offered up, that which is passive and offers no resistance, must still be actively “taken”; that is, the relationship between the observer, or the taker, and the pieces of information that observer constructs as evidence is always an active one involving negotiation, selection, and perhaps appropriation. This appropriative or exploitative gesture finds expression in a range of common metaphors that frame data analysis as more or less benign, forceful, or violating: we collect or “gather” data, like flowers or berries, but we also “capture” it, like prey, or “mine” and “extract” it; “raw” data, like sugar or crude oil, must then be “processed.” In all cases, data is conceived as a resource available to be exploited for its instrumental value; the refined products made with the data are sold back to private consumers, in the case of data-driven, targeted marketing, for example, or circulated to research communities and service providers through networks or exchanges involving differentiated social, political, and economic relations.

If, as Cornelius Puschmann and Jean Burgess have shown, mass media outlets, business journals, and popular technology magazines...